diff --git a/src/brevitas/core/scaling/standalone.py b/src/brevitas/core/scaling/standalone.py
index c8e612909..965bd3dce 100644
--- a/src/brevitas/core/scaling/standalone.py
+++ b/src/brevitas/core/scaling/standalone.py
@@ -212,10 +212,9 @@ def forward(self, ignored: torch.Tensor, threshold: torch.Tensor) -> torch.Tenso
             stats = stats + 0. * self.value
             if self.local_loss_mode:
                 return self.stats_scaling_impl(stats, threshold)
-            stats = self.restrict_inplace_preprocess(stats)
+            stats = self.restrict_inplace_preprocess(stats / threshold)
             inplace_tensor_mul(self.value.detach(), stats)
-            value = abs_binary_sign_grad(
-                self.stats_scaling_impl.restrict_clamp_scaling(self.value / threshold))
+            value = abs_binary_sign_grad(self.stats_scaling_impl.restrict_clamp_scaling(self.value))
             self.init_done = True
             return value
 
@@ -338,14 +337,12 @@ def training_forward(self, stats_input: Tensor, threshold: torch.Tensor) -> Tens
             self.counter = new_counter
             return abs_binary_sign_grad(clamped_stats) / threshold
         elif self.counter == self.collect_stats_steps:
-            self.restrict_inplace_preprocess(self.buffer)
+            self.restrict_inplace_preprocess(self.buffer / threshold)
             inplace_tensor_mul(self.value.detach(), self.buffer)
             self.counter = self.counter + 1
-            return abs_binary_sign_grad(
-                self.clamp_scaling(self.restrict_scaling(self.value / threshold)))
+            return abs_binary_sign_grad(self.clamp_scaling(self.restrict_scaling(self.value)))
         else:
-            return abs_binary_sign_grad(
-                self.clamp_scaling(self.restrict_scaling(self.value / threshold)))
+            return abs_binary_sign_grad(self.clamp_scaling(self.restrict_scaling(self.valu)))
 
     @brevitas.jit.script_method
     def forward(self, stats_input: Tensor, threshold: torch.Tensor) -> Tensor:
@@ -356,7 +353,7 @@ def forward(self, stats_input: Tensor, threshold: torch.Tensor) -> Tensor:
                 out = self.buffer / threshold
                 out = self.restrict_preprocess(out)
             else:
-                out = self.value / threshold
+                out = self.value
             out = abs_binary_sign_grad(self.clamp_scaling(self.restrict_scaling(out)))
         return out
 
diff --git a/tests/brevitas/graph/test_calibration.py b/tests/brevitas/graph/test_calibration.py
index 52819c22f..10d8f7e7c 100644
--- a/tests/brevitas/graph/test_calibration.py
+++ b/tests/brevitas/graph/test_calibration.py
@@ -47,9 +47,9 @@ def reference_implementation_scale_factors_po2(
     quant = compute_quantile(x, q)
     quant = torch.max(min_val, quant)
     quant_float_to_int = torch.ceil(
-        torch.log2(quant))  # Float to Int Implementation for PowerOfTwo scale
+        torch.log2(quant / int_scale))  # Float to Int Implementation for PowerOfTwo scale
 
-    scale = torch.pow(torch.tensor(2.), quant_float_to_int) / int_scale
+    scale = torch.pow(torch.tensor(2.), quant_float_to_int)
 
     return scale